Time selection to specify a relative time for event display

ABSTRACT

Event time selection output techniques are described. In one or more implementations, one or more inputs are received, at one or more computing devices, that involve interaction associated with a particular one of a plurality of events via a user interface, in which the plurality of events result from a search of data, each of the plurality of events include the data that is associated with a respective point in time, and the one or more inputs specify a relative time in relation to the respective point in time of the particular event. A determination is made as to which of the plurality of events correspond to the specified relative time by the one or more computing devices and a result of the determination is output by the one or more computing devices for display in the user interface.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is a Continuation of U.S. patent application Ser. No.16/177,027, filed Oct. 31, 2018, which is itself a Continuation of U.S.patent application Ser. No. 14/525,048, filed Oct. 27, 2014, whichclaims priority under 35 U.S.C. Section 119(e) as a non-provisionalapplication of U.S. Provisional Application No. 62/057,453, filed Sep.30, 2014, the disclosures of which are hereby incorporated by referencein their entirety.

BACKGROUND

Businesses and their data analysts face the challenge of making sense ofand finding patterns in the increasingly large amounts of data in themany types and formats that such businesses generate and collect. Forexample, accessing computer networks and transmitting electroniccommunications across the networks generates massive amounts of data,including such types of data as machine data and Web logs. Identifyingpatterns in this data, once thought relatively useless, has proven to beof great value to the businesses. In some instances, pattern analysiscan indicate which patterns are normal and which ones are unusual. Forexample, detecting unusual patterns can allow a computer system managerto investigate the circumstances and determine whether a computer systemsecurity threat exists.

Additionally, analysis of the data allows businesses to understand howtheir employees, potential consumers, and/or Web visitors use thecompany's online resources. Such analysis can provide businesses withoperational intelligence, business intelligence, and an ability tobetter manage their IT resources. For instance, such analysis may enablea business to better retain customers, meet customer needs, or improvethe efficiency of the company's IT resources. Despite the value that onecan derive from the underlying data described, making sense of this datato realize that value takes effort. In particular, patterns inunderlying data may be difficult to identify or understand whenanalyzing specific behaviors in isolation, often resulting in thefailure of a data analyst to notice valuable correlations in the datafrom which a business can draw strategic insight.

SUMMARY

Event time selection output techniques are described. In one or moreimplementations, one or more inputs are received, at one or morecomputing devices, that involve interaction associated with a particularone of a plurality of events via a user interface, in which theplurality of events result from a search of data, each of the pluralityof events include the data that is associated with a respective point intime, and the one or more inputs specify a relative time in relation tothe respective point in time of the particular event. A determination ismade as to which of the plurality of events correspond to the specifiedrelative time by the one or more computing devices and a result of thedetermination is output by the one or more computing devices for displayin the user interface.

This Summary introduces a selection of concepts in a simplified formthat are further described below in the Detailed Description. As such,this Summary is not intended to identify essential features of theclaimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different instances in thedescription and the figures may indicate similar or identical items.Entities represented in the figures may be indicative of one or moreentities and thus reference may be made interchangeably to single orplural forms of the entities in the discussion.

FIG. 1 presents a block diagram of an event-processing system inaccordance with the disclosed implementations.

FIG. 2 presents a flowchart illustrating how indexers process, index,and store data received from forwarders in accordance with the disclosedimplementations.

FIG. 3 presents a flowchart illustrating how a search head and indexersperform a search query in accordance with the disclosed implementations.

FIG. 4 presents a block diagram of a system for processing searchrequests that uses extraction rules for field values in accordance withthe disclosed implementations.

FIG. 5 illustrates an exemplary search query received from a client andexecuted by search peers in accordance with the disclosedimplementations.

FIG. 6A illustrates a search screen in accordance with the disclosedimplementations.

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed implementations.

FIG. 7A illustrates a key indicators view in accordance with thedisclosed implementations.

FIG. 7B illustrates an incident review dashboard in accordance with thedisclosed implementations.

FIG. 7C illustrates a proactive monitoring tree in accordance with thedisclosed implementations.

FIG. 7D illustrates a screen displaying both log data and performancedata in accordance with the disclosed implementations.

FIG. 8 depicts an example of a user interface that displays a pluralityof events resulting from a search performed using one or more extractionrules of a late-binding schema.

FIG. 9 depicts an example of a user interface that is output in responseto user interaction involving selection of a toggle of FIG. 8 .

FIG. 10 depicts an example of a user interface that includes an outputof an option that is selectable to specify a relative time in relationto a point in time of a corresponding event.

FIG. 11 depicts an example of a user interface showing selection of“+/−1 day” portion of the option of FIG. 10 .

FIG. 12 depicts another example of a user interface showing anotherexample of an option that is usable to specify a relative time inrelation to a point in time of an event.

FIG. 13 depicts a user interface showing specification of a unit of timeusing the option of FIG. 12 .

FIG. 14 depicts a user interface showing specification of a unit of timeusing the option of FIG. 12 .

FIG. 15 depicts a user interface showing another example of an optionthat is usable to specify a relative time in relation to a point in timeof an event.

FIG. 16 depicts a user interface showing yet another example of anoption that is usable to specify a relative time in relation to a pointin time of an event.

FIGS. 17 and 18 illustrate additional examples of user interfaces inwhich the option is associated with a search input portion.

FIG. 19 is a flow diagram depicting a procedure in an exampleimplementation in which event time selection techniques are described.

FIG. 20 illustrates an example system including various components of anexample device that can be implemented as any type of computing deviceas described and/or utilize with reference to FIGS. 1-19 to implementembodiments of the techniques described herein.

DETAILED DESCRIPTION

Overview

Data analysts are often confronted with navigating through vast amountsof data to locate data of interest. Techniques to perform thisnavigation often involve use of search queries which are run and afterwhich a user may view the results. Conventional techniques to submitsearch queries, however, typically involve manual entry by a user andsubsequent manual modification in order to navigate through the data,which may be time consuming and frustrating.

Event time selection output techniques are described. In one or moreimplementations, a user may submit a search query, such as through useof one or more extraction rules as part of a late binding schema to viewevents having fields and corresponding values for those fields in a userinterface. An option is also provided, via which, a user may specify arelative time in relation to a point in time that corresponds to theevents, such as a range of time before and/or after the point in timethat corresponds to the event. In this way, the option may supportefficient navigation to events that may be of interest that surround aparticular event. Further discussion of these and other examples may befound in relation to the following sections.

In the following discussion, an example environment is first describedthat may employ the techniques described herein. Example event timeselection output techniques and procedures are then described which maybe performed in the example environment as well as other environments.Consequently, performance of the example techniques and procedures isnot limited to the example environment and the example environment isnot limited to performance of the example techniques and procedures.

Example Environment

Modern data centers often comprise thousands of host computer systemsthat operate collectively to service requests from even larger numbersof remote clients. During operation, these data centers generatesignificant volumes of performance data and diagnostic information thatcan be analyzed to quickly diagnose performance problems. In order toreduce the size of this performance data, the data is typicallypre-processed prior to being stored based on anticipated data-analysisneeds. For example, pre-specified data items can be extracted from theperformance data and stored in a database to facilitate efficientretrieval and analysis at search time. However, the rest of theperformance data is not saved and is essentially discarded duringpre-processing. As storage capacity becomes progressively cheaper andmore plentiful, there are fewer incentives to discard this performancedata and many reasons to keep it.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed performance data at “ingestiontime” for later retrieval and analysis at “search time.” Note thatperforming the analysis operations at search time provides greaterflexibility because it enables an analyst to search all of theperformance data, instead of searching pre-specified data items thatwere stored at ingestion time. This enables the analyst to investigatedifferent aspects of the performance data instead of being confined tothe pre-specified set of data items that were selected at ingestiontime.

However, analyzing massive quantities of heterogeneous performance dataat search time can be a challenging task. A data center may generateheterogeneous performance data from thousands of different components,which can collectively generate tremendous volumes of performance datathat can be time-consuming to analyze. For example, this performancedata can include data from system logs, network packet data, sensordata, and data generated by various applications. Also, the unstructurednature of much of this performance data can pose additional challengesbecause of the difficulty of applying semantic meaning to unstructureddata, and the difficulty of indexing and querying unstructured datausing traditional database systems.

These challenges can be addressed by using an event-based system, suchas the SPLUNK® ENTERPRISE system produced by Splunk Inc. of SanFrancisco, Calif., to store and process performance data. The SPLUNK®ENTERPRISE system is the leading platform for providing real-timeoperational intelligence that enables organizations to collect, index,and harness machine-generated data from various websites, applications,servers, networks, and mobile devices that power their businesses. TheSPLUNK® ENTERPRISE system is particularly useful for analyzingunstructured performance data, which is commonly found in system logfiles. Although many of the techniques described herein are explainedwith reference to the SPLUNK® ENTERPRISE system, the techniques are alsoapplicable to other types of data server systems.

In the SPLUNK® ENTERPRISE system, performance data is stored as“events,” in which each event comprises a collection of performance dataand/or diagnostic information that is generated by a computer system andis correlated with a specific point in time. Events can be derived from“time series data,” in which time series data includes a sequence ofdata points (e.g., performance measurements from a computer system) thatare associated with successive points in time and are typically spacedat uniform time intervals. Events can also be derived from “structured”or “unstructured” data. Structured data has a predefined format, inwhich specific data items with specific data formats reside atpredefined locations in the data. For example, structured data caninclude data items stored in fields in a database table. In contrast,unstructured data does not have a predefined format. This means thatunstructured data can include various data items having different datatypes that can reside at different locations. For example, when the datasource is an operating system log, an event can include one or morelines from the operating system log containing raw data that includesdifferent types of performance and diagnostic information associatedwith a specific point in time. Examples of data sources from which anevent may be derived include, but are not limited to: web servers;application servers; databases; firewalls; routers; operating systems;and software applications that execute on computer systems, mobiledevices, and sensors. The data generated by such data sources can beproduced in various forms including, for example and without limitation,server log files, activity log files, configuration files, messages,network packet data, performance measurements and sensor measurements.An event typically includes a timestamp that may be derived from the rawdata in the event, or may be determined through interpolation betweentemporally proximate events having known timestamps.

The SPLUNK® ENTERPRISE system also facilitates using a flexible schemato specify how to extract information from the event data, in which theflexible schema may be developed and redefined as needed. Note that aflexible schema may be applied to event data “on the fly” as desired(e.g., at search time), rather than at ingestion time of the data as intraditional database systems. Because the schema is not applied to eventdata until it is desired (e.g., at search time), it is referred to as a“late-binding schema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw data,which can include unstructured data, machine data, performancemeasurements or other time-series data, such as data obtained fromweblogs, syslogs, or sensor readings. It divides this raw data into“portions,” and optionally transforms the data to produce timestampedevents. The system stores the timestamped events in a data store, andenables a user to run queries against the data store to retrieve eventsthat meet specified criteria, such as containing certain keywords orhaving specific values in defined fields. Note that the term “field”refers to a location in the event data containing a value for a specificdata item.

As noted above, the SPLUNK® ENTERPRISE system facilitates using alate-binding schema while performing queries on events. A late-bindingschema specifies “extraction rules” that are applied to data in theevents to extract values for specific fields. More specifically, theextraction rules for a field can include one or more instructions thatspecify how to extract a value for the field from the event data. Anextraction rule can generally include any type of instruction forextracting values from data in events. In some cases, an extraction ruleincludes a regular expression, in which case the rule is referred to asa “regex rule.”

In contrast to a conventional schema for a database system, alate-binding schema is not defined at data ingestion time. Instead, thelate-binding schema can be developed on an ongoing basis until the timea query is actually executed. This means that extraction rules for thefields in a query may be provided in the query itself, or may be locatedduring execution of the query. Hence, as an analyst learns more aboutthe data in the events, the analyst can continue to refine thelate-binding schema by adding new fields, deleting fields, or changingthe field extraction rules until the next time the schema is used by aquery. Because the SPLUNK® ENTERPRISE system maintains the underlyingraw data and provides a late-binding schema for searching the raw data,it enables an analyst to investigate questions that arise as the analystlearns more about the events.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain fields in the eventswhen the events are being created, indexed, or stored, or possibly at alater time. Alternatively, a user may manually define extraction rulesfor fields using a variety of techniques.

Also, a number of “default fields” that specify metadata about theevents rather than data in the events themselves can be createdautomatically. For example, such default fields can specify: a timestampfor the event data; a host from which the event data originated; asource of the event data; and a source type for the event data. Thesedefault fields may be determined automatically when the events arecreated, indexed or stored.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by different data sources, thesystem facilitates use of a “common information model” (CIM) across thedifferent data sources.

1.2 Data Server System

FIG. 1 presents a block diagram of an exemplary event-processing system100, similar to the SPLUNK® ENTERPRISE system. System 100 includes oneor more forwarders 101 that collect data obtained from a variety ofdifferent data sources 105, and one or more indexers 102 that store,process, and/or perform operations on this data, in which each indexeroperates on data contained in a specific data store 103. Theseforwarders and indexers can comprise separate computer systems in a datacenter, or may alternatively comprise separate processes executing onvarious computer systems in a data center.

During operation, the forwarders 101 identify which indexers 102 willreceive the collected data and then forward the data to the identifiedindexers. Forwarders 101 can also perform operations to strip outextraneous data and detect timestamps in the data. The forwarders nextdetermine which indexers 102 will receive each data item and thenforward the data items to the determined indexers 102.

Note that distributing data across different indexers facilitatesparallel processing. This parallel processing can take place at dataingestion time, because multiple indexers can process the incoming datain parallel. The parallel processing can also take place at search time,because multiple indexers can search through the data in parallel.

System 100 and the processes described below with respect to FIGS. 1-5are further described in “Exploring Splunk Search Processing Language(SPL) Primer and Cookbook” by David Carasso, CITO Research, 2012, and in“Optimizing Data Analysis With a Semi-Structured Time Series Database”by Ledion Bitincka, Archana Ganapathi, Stephen Sorkin, and Steve Zhang,SLAML, 2010, each of which is hereby incorporated herein by reference inits entirety for all purposes.

1.3 Data Ingestion

FIG. 2 presents a flowchart 200 illustrating how an indexer processes,indexes, and stores data received from forwarders in accordance with thedisclosed embodiments. At block 201, the indexer receives the data fromthe forwarder. Next, at block 202, the indexer apportions the data intoevents. Note that the data can include lines of text that are separatedby carriage returns or line breaks and an event may include one or moreof these lines. During the apportioning process, the indexer can useheuristic rules to automatically determine the boundaries of the events,which for example coincide with line boundaries. These heuristic rulesmay be determined based on the source of the data, in which the indexercan be explicitly informed about the source of the data or can infer thesource of the data by examining the data. These heuristic rules caninclude regular expression-based rules or delimiter-based rules fordetermining event boundaries, in which the event boundaries may beindicated by predefined characters or character strings. Thesepredefined characters may include punctuation marks or other specialcharacters including, for example, carriage returns, tabs, spaces orline breaks. In some cases, a user can fine-tune or configure the rulesthat the indexers use to determine event boundaries in order to adaptthe rules to the user's specific requirements.

Next, the indexer determines a timestamp for each event at block 203. Asmentioned above, these timestamps can be determined by extracting thetime directly from data in the event, or by interpolating the time basedon timestamps from temporally proximate events. In some cases, atimestamp can be determined based on the time the data was received orgenerated. The indexer subsequently associates the determined timestampwith each event at block 204, for example by storing the timestamp asmetadata for each event.

Then, the system can apply transformations to data to be included inevents at block 205. For log data, such transformations can includeremoving a portion of an event (e.g., a portion used to define eventboundaries, extraneous text, characters, etc.) or removing redundantportions of an event. Note that a user can specify portions to beremoved using a regular expression or any other possible technique.

Next, a keyword index can optionally be generated to facilitate fastkeyword searching for events. To build a keyword index, the indexerfirst identifies a set of keywords in block 206. Then, at block 207 theindexer includes the identified keywords in an index, which associateseach stored keyword with references to events containing that keyword(or to locations within events where that keyword is located). When anindexer subsequently receives a keyword-based query, the indexer canaccess the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, wherein a name-value pair can includea pair of keywords connected by a symbol, such as an equals sign orcolon. In this way, events containing these name-value pairs can bequickly located. In some embodiments, fields can automatically begenerated for some or all of the name-value pairs at the time ofindexing. For example, if the string “dest=10.0.1.2” is found in anevent, a field named “dest” may be created for the event, and assigned avalue of “10.0.1.2.”

Finally, the indexer stores the events in a data store at block 208,wherein a timestamp can be stored with each event to facilitatesearching for events based on a time range. In some cases, the storedevents are organized into a plurality of buckets, wherein each bucketstores events associated with a specific time range. This not onlyimproves time-based searches, but it also allows events with recenttimestamps that may have a higher likelihood of being accessed to bestored in faster memory to facilitate faster retrieval. For example, abucket containing the most recent events can be stored as flash memoryinstead of on hard disk.

Each indexer 102 is responsible for storing and searching a subset ofthe events contained in a corresponding data store 103. By distributingevents among the indexers and data stores, the indexers can analyzeevents for a query in parallel, for example using map-reduce techniques,in which each indexer returns partial responses for a subset of eventsto a search head that combines the results to produce an answer for thequery. By storing events in buckets for specific time ranges, an indexermay further optimize searching by looking only in buckets for timeranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as is described in U.S. patent application Ser. No. 14/266,812filed on 30 Apr. 2014, and in U.S. patent application Ser. No.14/266,817 also filed on 30 Apr. 2014.

1.4 Query Processing

FIG. 3 presents a flowchart 300 illustrating how a search head andindexers perform a search query in accordance with the disclosedembodiments. At the start of this process, a search head receives asearch query from a client at block 301. Next, at block 302, the searchhead analyzes the search query to determine what portions can bedelegated to indexers and what portions need to be executed locally bythe search head. At block 303, the search head distributes thedetermined portions of the query to the indexers. Note that commandsthat operate on single events can be trivially delegated to theindexers, while commands that involve events from multiple indexers areharder to delegate.

Then, at block 304, the indexers to which the query was distributedsearch their data stores for events that are responsive to the query. Todetermine which events are responsive to the query, the indexer searchesfor events that match the criteria specified in the query. This criteriacan include matching keywords or specific values for certain fields. Ina query that uses a late-binding schema, the searching operations inblock 304 may involve using the late-binding scheme to extract valuesfor specified fields from events at the time the query is processed.Next, the indexers can either send the relevant events back to thesearch head, or use the events to calculate a partial result, and sendthe partial result back to the search head.

Finally, at block 305, the search head combines the partial resultsand/or events received from the indexers to produce a final result forthe query. This final result can comprise different types of datadepending upon what the query is asking for. For example, the finalresults can include a listing of matching events returned by the query,or some type of visualization of data from the returned events. Inanother example, the final result can include one or more calculatedvalues derived from the matching events.

Moreover, the results generated by system 100 can be returned to aclient using different techniques. For example, one technique streamsresults back to a client in real-time as they are identified. Anothertechnique waits to report results to the client until a complete set ofresults is ready to return to the client. Yet another technique streamsinterim results back to the client in real-time until a complete set ofresults is ready, and then returns the complete set of results to theclient. In another technique, certain results are stored as “searchjobs,” and the client may subsequently retrieve the results byreferencing the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head starts executing aquery, the search head can determine a time range for the query and aset of common keywords that all matching events must include. Next, thesearch head can use these parameters to query the indexers to obtain asuperset of the eventual results. Then, during a filtering stage, thesearch head can perform field-extraction operations on the superset toproduce a reduced set of search results.

1.5 Field Extraction

FIG. 4 presents a block diagram 400 illustrating how fields can beextracted during query processing in accordance with the disclosedembodiments. At the start of this process, a search query 402 isreceived at a query processor 404. Query processor 404 includes variousmechanisms for processing a query, wherein these mechanisms can residein a search head 104 and/or an indexer 102. Note that the exemplarysearch query 402 illustrated in FIG. 4 is expressed in Search ProcessingLanguage (SPL), which is used in conjunction with the SPLUNK® ENTERPRISEsystem. SPL is a pipelined search language in which a set of inputs isoperated on by a first command in a command line, and then a subsequentcommand following the pipe symbol “|” operates on the results producedby the first command, and so on for additional commands. Search query402 can also be expressed in other query languages, such as theStructured Query Language (“SQL”) or any suitable query language.

Upon receiving search query 402, query processor 404 sees that searchquery 402 includes two fields “IP” and “target.” Query processor 404also determines that the values for the “IP” and “target” fields havenot already been extracted from events in data store 414, andconsequently determines that query processor 404 needs to use extractionrules to extract values for the fields. Hence, query processor 404performs a lookup for the extraction rules in a rule base 406, in whichrule base 406 maps field names to corresponding extraction rules andobtains extraction rules 408-409, extraction rule 408 specifies how toextract a value for the “IP” field from an event, and extraction rule409 specifies how to extract a value for the “target” field from anevent. As is illustrated in FIG. 4 , extraction rules 408-409 caninclude regular expressions that specify how to extract values for therelevant fields. Such regular-expression-based extraction rules are alsoreferred to as “regex rules.” In addition to specifying how to extractfield values, the extraction rules may also include instructions forderiving a field value by performing a function on a character string orvalue retrieved by the extraction rule. For example, a transformationrule may truncate a character string, or convert the character stringinto a different data format. In some cases, the query itself canspecify one or more extraction rules.

Next, query processor 404 sends extraction rules 408-409 to a fieldextractor 412, which applies extraction rules 408-409 to events 416-418in a data store 414. Note that data store 414 can include one or moredata stores, and extraction rules 408-409 can be applied to largenumbers of events in data store 414, and are not meant to be limited tothe three events 416-418 illustrated in FIG. 4 . Moreover, the queryprocessor 404 can instruct field extractor 412 to apply the extractionrules to all the events in a data store 414, or to a subset of theevents that have been filtered based on some criteria.

Next, field extractor 412 applies extraction rule 408 for the firstcommand “Search IP=“10*” to events in data store 414 including events416-418. Extraction rule 408 is used to extract values for the IPaddress field from events in data store 414 by looking for a pattern ofone or more digits, followed by a period, followed again by one or moredigits, followed by another period, followed again by one or moredigits, followed by another period, and followed again by one or moredigits. Next, field extractor 412 returns field values 420 to queryprocessor 404, which uses the criterion IP=“10*” to look for IPaddresses that start with “10”. Note that events 416 and 417 match thiscriterion, but event 418 does not, so the result set for the firstcommand is events 416-417.

Query processor 404 then sends events 416-417 to the next command “statscount target.” To process this command, query processor 404 causes fieldextractor 412 to apply extraction rule 409 to events 416-417. Extractionrule 409 is used to extract values for the target field for events416-417 by skipping the first four commas in events 416-417, and thenextracting all of the following characters until a comma or period isreached. Next, field extractor 412 returns field values 421 to queryprocessor 404, which executes the command “stats count target” to countthe number of unique values contained in the target fields, which inthis example produces the value “2” that is returned as a final result422 for the query.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include: a set of one or more events; a set of one or morevalues obtained from the events; a subset of the values; statisticscalculated based on the values; a report containing the values; or avisualization, such as a graph or chart, generated from the values.

1.6 Example Search Screen

FIG. 6A illustrates an exemplary search screen 600 in accordance withthe disclosed embodiments. Search screen 600 includes a search bar 602that accepts user input in the form of a search string. It also includesa time range picker 612 that enables the user to specify a time rangefor the search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, for example by selecting specifichosts and log files.

After the search is executed, the search screen 600 can display theresults through search results tabs 604, wherein search results tabs 604includes: an “events tab” that displays various information about eventsreturned by the search; a “statistics tab” that displays statisticsabout the search results; and a “visualization tab” that displaysvarious visualizations of the search results. The events tab illustratedin FIG. 6A displays a timeline graph 605 that graphically illustratesthe number of events that occurred in one-hour intervals over theselected time range. It also displays an events list 608 that enables auser to view the raw data in each of the returned events. Itadditionally displays a fields sidebar 606 that includes statisticsabout occurrences of specific fields in the returned events, including“selected fields” that are pre-selected by the user, and “interestingfields” that are automatically selected by the system based onpre-specified criteria.

1.7 Acceleration Techniques

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed performancedata “on the fly” at search time instead of storing pre-specifiedportions of the performance data in a database at ingestion time. Thisflexibility enables a user to see correlations in the performance dataand perform subsequent queries to examine interesting aspects of theperformance data that may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause considerable delays whileprocessing the queries. Fortunately, a number of acceleration techniqueshave been developed to speed up analysis operations performed at searchtime. These techniques include: (1) performing search operations inparallel by formulating a search as a map-reduce computation; (2) usinga keyword index; (3) using a high performance analytics store; and (4)accelerating the process of generating reports. These techniques aredescribed in more detail below.

1.7.1 Map-Reduce Technique

To facilitate faster query processing, a query can be structured as amap-reduce computation, wherein the “map” operations are delegated tothe indexers, while the corresponding “reduce” operations are performedlocally at the search head. For example, FIG. 5 illustrates an example500 of how a search query 501 received from a client at search head 104can split into two phases, including: (1) a “map phase” comprisingsubtasks 502 (e.g., data retrieval or simple filtering) that may beperformed in parallel and are “mapped” to indexers 102 for execution,and (2) a “reduce phase” comprising a merging operation 503 to beexecuted by the search head when the results are ultimately collectedfrom the indexers.

During operation, upon receiving search query 501, search head 104modifies search query 501 by substituting “stats” with “prestats” toproduce search query 502, and then distributes search query 502 to oneor more distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as is illustrated in FIG. 3 , or mayalternatively distribute a modified version (e.g., a more restrictedversion) of the search query to the search peers. In this example, theindexers are responsible for producing the results and sending them tothe search head. After the indexers return the results to the searchhead, the search head performs the merging operations 503 on theresults. Note that by executing the computation in this way, the systemeffectively distributes the computational operations while minimizingdata transfers.

1.7.2 Keyword Index

As described above with reference to the flow charts 200, 300 in FIGS. 2and 3 , event-processing system 100 can construct and maintain one ormore keyword indices to facilitate rapidly identifying events containingspecific keywords. This can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

1.7.3 High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 100make use of a high performance analytics store, which is referred to asa “summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an exemplary entry in a summarization table can keep track ofoccurrences of the value “94107” in a “ZIP code” field of a set ofevents, wherein the entry includes references to all of the events thatcontain the value “94107” in the ZIP code field. This enables the systemto quickly process queries that seek to determine how many events have aparticular value for a particular field, because the system can examinethe entry in the summarization table to count instances of the specificvalue in the field without having to go through the individual events ordo extractions at search time. Also, if the system needs to process eachof the events that have a specific field-value combination, the systemcan use the references in the summarization table entry to directlyaccess the events to extract further information without having tosearch each of the events to find the specific field-value combinationat search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range, wherein a bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer, in which theindexer-specific summarization table only includes entries for theevents in a data store that is managed by the specific indexer.

The summarization table can be populated by running a “collection query”that scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A collection query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Acollection query can also be automatically launched in response to aquery that asks for a specific field-value combination.

In some cases, the summarization tables may not cover each of the eventsthat are relevant to a query. In this case, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query.This summarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, issued on Mar. 25, 2014.

1.7.4 Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. (This is possible if results from preceding timeperiods can be computed separately and combined to generate an updatedreport. In some cases, it is not possible to combine such incrementalresults, for example where a value in the report depends onrelationships between events from different time periods.) If reportscan be accelerated, the summarization engine periodically generates asummary covering data obtained during a latest non-overlapping timeperiod. For example, where the query seeks events meeting a specifiedcriteria, a summary for the time period includes only events within thetime period that meet the specified criteria. Similarly, if the queryseeks statistics calculated from the events, such as the number ofevents that match the specified criteria, then the summary for the timeperiod includes the number of events in the period that match thespecified criteria.

In parallel with the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so only the newer eventdata needs to be processed while generating an updated report. Thesereport acceleration techniques are described in more detail in U.S. Pat.No. 8,589,403, issued on Nov. 19, 2013, and U.S. Pat. No. 8,412,696,issued on Apr. 2, 2011.

1.8 Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that make it easy for developers to create applicationsto provide additional capabilities. One such application is the SPLUNK®APP FOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. This differs significantly fromconventional Security Information and Event Management (STEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related event data. Traditional STEM systemstypically use fixed schemas to extract data from pre-definedsecurity-related fields at data ingestion time, wherein the extracteddata is typically stored in a relational database. This data extractionprocess (and associated reduction in data size) that occurs at dataingestion time inevitably hampers future incident investigations, whenall of the original data may be needed to determine the root cause of asecurity issue, or to detect the tiny fingerprints of an impendingsecurity threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. (The process of detecting securitythreats for network-related information is further described in U.S.patent application Ser. Nos. 13/956,252, and 13/956,262.)Security-related information can also include endpoint information, suchas malware infection data and system configuration information, as wellas access control information, such as login/logout information andaccess failure notifications. The security-related information canoriginate from various sources within a data center, such as hosts,virtual machines, storage devices and sensors. The security-relatedinformation can also originate from various sources in a network, suchas routers, switches, email servers, proxy servers, gateways, firewallsand intrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting so-called “notable events” that are likely to indicate asecurity threat. These notable events can be detected in a number ofways: (1) an analyst can notice a correlation in the data and canmanually identify a corresponding group of one or more events as“notable;” or (2) an analyst can define a “correlation search”specifying criteria for a notable event, and every time one or moreevents satisfy the criteria, the application can indicate that the oneor more events are notable. An analyst can alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches can be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents can be stored in a dedicated “notable events index,” which can besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts can be generated to notifysystem operators when important notable events are discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics of interest, such as countsof different types of notable events. For example, FIG. 7A illustratesan exemplary key indicators view 700 that comprises a dashboard, whichcan display a value 701, for various security-related metrics, such asmalware infections 702. It can also display a change in a metric value703, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 700 additionallydisplays a histogram panel 705 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338filed Jul. 31, 2013.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 7B illustrates an exemplary incident review dashboard 710 thatincludes a set of incident attribute fields 711 that, for example,enables a user to specify a time range field 712 for the displayedevents. It also includes a timeline 713 that graphically illustrates thenumber of incidents that occurred in one-hour time intervals over theselected time range. It additionally displays an events list 714 thatenables a user to view a list of each of the notable events that matchthe criteria in the incident attributes fields 711. To facilitateidentifying patterns among the notable events, each notable event can beassociated with an urgency value (e.g., low, medium, high, critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event can be determined based on the severity of theevent and the priority of the system component associated with theevent. The incident review dashboard is described further in“http://docs.splunk.com/Documentation/PCI/2.1.1/User/IncidentReviewdashboard.”

1.9 Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that make it easy for developers to create variousapplications. One such application is the SPLUNK® APP FOR VMWARE®, whichperforms monitoring operations and includes analytics to facilitatediagnosing the root cause of performance problems in a data center basedon large volumes of data stored by the SPLUNK® ENTERPRISE system.

This differs from conventional data-center-monitoring systems that lackthe infrastructure to effectively store and analyze large volumes ofperformance information and log data obtained from the data center. Inconventional data-center-monitoring systems, this performance data istypically pre-processed prior to being stored, for example by extractingpre-specified data items from the performance data and storing them in adatabase to facilitate subsequent retrieval and analysis at search time.However, the rest of the performance data is not saved and isessentially discarded during pre-processing. In contrast, the SPLUNK®APP FOR VMWARE® stores large volumes of minimally processed performanceinformation and log data at ingestion time for later retrieval andanalysis at search time when a live performance issue is beinginvestigated.

The SPLUNK® APP FOR VMWARE® can process many types ofperformance-related information. In general, this performance-relatedinformation can include any type of performance-related data and logdata produced by virtual machines and host computer systems in a datacenter. In addition to data obtained from various log files, thisperformance-related information can include values for performancemetrics obtained through an application programming interface (API)provided as part of the vSphere Hypervisor™ system distributed byVMware, Inc. of Palo Alto, Calif. For example, these performance metricscan include: (1) CPU-related performance metrics; (2) disk-relatedperformance metrics; (3) memory-related performance metrics; (4)network-related performance metrics; (5) energy-usage statistics; (6)data-traffic-related performance metrics; (7) overall systemavailability performance metrics; (8) cluster-related performancemetrics; and (9) virtual machine performance statistics. For moredetails about such performance metrics, please see U.S. patent Ser. No.14/167,316 filed 29 Jan. 2014, which is hereby incorporated herein byreference. Also, see “vSphere Monitoring and Performance,” Update 1,vSphere 5.5, EN-001357-00,http://pubs.vmware.com/vsphere-55/topic/com.vmware.ICbase/PDF/vsphere-esxi-vcenter-server-551-monitoring-performance-guide.pdf.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Exemplary node-expansion operations are illustrated in FIG.7C, wherein nodes 733 and 734 are selectively expanded. Note that nodes731-739 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/235,490 filed on 15 Apr. 2014, which is hereby incorporated herein byreference for all possible purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous data,comprising events, log data and associated performance metrics, for theselected time range. For example, the screen illustrated in FIG. 7Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 742 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316 filed on 29Jan. 2014, which is hereby incorporated herein by reference for allpossible purposes.

Event Time Selection

FIG. 8 depicts an example of a user interface 800 that displays aplurality of events resulting from a search performed using one or moreextraction rules of a late-binding schema. As previously described,searches performed by the system 100 of FIG. 1 may involve vast amountsof data to locate events 802, 804, 806, 808 that correspond to thesearch using one or more extractions rules, e.g., “on the fly.” Events802-808, for instance, may comprise a collection of data that iscorrelated with a respective point in time. As shown in the userinterface 800, for instance, event 802 corresponds to “12:25:21.201 AM”on “Sep. 17, 2014,” event 804 corresponds to “12:25:21.191 AM” on “Sep.17, 2014,” event 806 corresponds to “12:25:21.175 AM” on “Sep. 17,2014,” and event 808 corresponds to “12:25:21.159 AM” on “Sep. 17,2014.” Each of these events 802-808 in the user interface 800 alsoincludes a respective portion of data that corresponds to the event. Forexample, event 802 includes data 810 that is based on selected fields814 as well as raw data 818 that is the content (e.g., payload) of theevent 802.

Oftentimes when analyzing data, a user may become interested in eventsclose to a point in time of a particular event. For example, the usermay perform a search and become interested in a particular event, suchas event 802. Upon investigating the event 802, the user may desireadditional information on “what happened” surrounding the event.Conventional techniques to obtain this additional information, however,typically involve manual reentry of the extraction rule along withadditional conditions specifying a desired time range, which could beinefficient and become frustrating to a user. For example, thesetechniques may involve manual reentry of search conditions in the searchbar 816 for each successive search.

Accordingly, event time selection techniques are described in whichinteraction may be supported with an event to cause output of an optionto specify a relative time in relation to the point in time of theevent, such a range of time and so on. For example, in the illustrateduser interface 800 a user may select a toggle 812 to display a userinterface include a listing of fields associated with the event, anexample of which is discussed as follows and shown in a correspondingfigure.

FIG. 9 depicts an example of a user interface 900 that is output inresponse to user interaction involving selection of a toggle 812 of FIG.8 . The user interface 900 in this example is configured as anevent-limited field picker that is operable to enable selection of dataassociated with individual events to display in the view of events, ifapplicable. The event-limited field picker, for instance, may also beutilized to specify metadata 902 associated with the event. Asillustrated, for instance, a user may select host, source, and sourcetype fields that cause display of corresponding values of the metadatain the user interface 800 of FIG. 8 .

The event-limited field picker of the user interface 900 may alsosupport options for a user to specify that values for fields 904 of thesearch are to be displayed in the user interface 800 of FIG. 8 . Aspreviously described, each field 904 in the event-limited field pickeris defined according to an extraction rule that includes one or moreinstructions specifying how to extract a value for a field defined bythe rule from the data stored as events. For examples, events such asbytes, method, file, user, version, and so on may be selected to causedisplay of values from corresponding fields in the user interface 800 ofFIG. 8 .

The user interface also includes a display of a representation 906 ofthe point in time associated with the event 802 of FIG. 8 . Therepresentation 906 is selectable to cause output of an option to specifya relative time in relation to the point in time depicted by therepresentation 906, an example of which is discussed in the followingand shown in a corresponding figure.

FIG. 10 depicts an example of a user interface 1000 that includes anoutput of an option 1002 that is selectable to specify a relative timein relation to a point in time of a corresponding event. In thisexample, the option 1002 is displayed as a pop-up window disposedproximal to the representation 906 of the point in time of the event.The option 1002 includes portions 1004 that are selectable to specify arelative time in relation to the point in time of the event, such as arelative time before the point in time, after the point in time, at thepoint in time, and so on.

Further, the option 1002 also includes portions that are selectable tospecify ranges, such as +/−one week, day, hour, minute, second,millisecond, and so on. The user interface 1100 of FIG. 11 depicts anexample of selection of “+/−1 day” portion 1104 of the option 1002. Inthis way, a user may interact with the option to specify a relative timein relation to the point in time associated with the event.

Once selected, a variety of different actions may be performed. Forexample, the extraction rules used to obtain the results of the searchmay be modified automatically and without user intervention to includethe relative time and redo the search. In this way, the relative pointin time may be included as part of the search automatically and withoutfurther user intervention, e.g., without manually reentering theextraction rules along with the relative point in time. For example, therelative time may be automatically inserted into a search bar andperformance of the search may be repeated to include the relative timeand the previous conditions. In another example, the specification ofthe relative point in time may be a basis of application of a newextraction rule that does not include conditions of the previousextraction rule and as such as user may “scope out” from the previousextraction rule to view events surrounding the displayed event. Forexample, the relative time may form a new extraction rule that isautomatically populated in the search bar without the previous terms toperform a new search. A variety of other examples are also contemplated.

FIG. 12 depicts another example of a user interface 1200 showing anotherexample of an option 1202 that is usable to specify a relative time inrelation to a point in time of an event. In the previous example of FIG.10 , the option 1002 included portions 1204 specifying static examplesof ranges that are selectable to specify relative times surrounding thepoint in time of the event. In the option 1202 of FIG. 12 , however, auser may manually specify a relative time using portions 1204 toindicate an amount of time before and/or after the point in time, anamount of time, and a unit of time. As illustrated in FIG. 12 , forinstance, a user selects a “+/−” portion 1206 of the option 1202 tospecify a time range before and after the point in time of the event.

FIG. 13 depicts a user interface 1300 showing specification of a unit oftime using the option 1202 of FIG. 12 . In this example, a userspecifies a number “5” 1302 that along with specification of a unit oftime “seconds” may be used to specify a relative amount of time inrelation to the point in time of the event. For example, FIG. 14 depictsa user interface 1400 showing specification of a unit of time using theoption 1202 of FIG. 12 . As illustrated, a portion 1402 of the userinterface may support user interaction to specify units of time, such asweeks, days, hours, minutes, seconds, milliseconds, and so on.

FIG. 15 depicts a user interface 1500 showing another example of anoption 1502 that is usable to specify a relative time in relation to apoint in time of an event. In this example, an event 1504 and portionsof data 1506 included in the event 1504 as specified by the field pickerand raw data 1512 as described above. The event 1504 also includes arepresentation 1508 of the point in time corresponding to the event,e.g., “Sep. 17, 2014” and “12:41:11.625 AM.” A user may select therepresentation 1506, e.g., through hovering or “clicking” a cursorcontrol device, gesture, voice command, and so on, which causes outputof the option 1502.

As previously described, the option 1502 includes portions that areconfigured to specify a relative time in relation to the point in timerepresented 1508 for the event 1504. As illustrated, this may includeportions 1510 to specify a relative time before, after, or at therepresented 1508 point in time, may be utilized to specify a range, andso on. Thus, in this example a user is provided with a non-modal option1502 (e.g., as a pop-up menu) to specify the relative time withoutnavigating away from the user interface, e.g., as was performed in FIGS.9-14 . Other non-modal examples are also contemplated, furtherdiscussion of which may be found in the following and is shown incorresponding figures.

FIG. 16 depicts a user interface 1600 showing yet another example of anoption 1602 that is usable to specify a relative time in relation to apoint in time of an event. The user interface 1600 in this exampleincludes a search input portion 1604 (e.g., a search bar) via which auser may specify extraction rules to be used to perform a search, e.g.,as part of a late-binding schema as previously described.

The user interface also includes the option 1602 that is associated aspart of the search input portion 1604. Selection of a feature 1606 thatis a representation of functionality to select a date/time range, forinstance, may cause output of the option 1602. The option 1602, asbefore, is configured to specify a relative time in relation to a pointin time of one or more of the events 1608 displayed in the userinterface 1600.

For example, the option 1602 in this example includes portions that areselectable to specify preset 1608 relative points in time (e.g., +/−aday as before) that are selectable to specify predefined relative pointsin time, relative 1610 points in time, a date range 1614, a date andtime range 1616, and so on. The option 1602 may also include a portionto specify application to real time 1612 data. Selection of an apply1610 option may then cause the specified data to be populated into thesearch input portion 1604 to perform the search using these conditions,e.g., as part of the previous search or a new search using just thoseconditions as previously described. FIGS. 17 and 18 illustrateadditional examples of user interfaces 1700, 1800 in which the option isassociated with a search input portion. Further discussion of these andother techniques may be found in relation to the following procedures.

Example Procedures

The following discussion describes techniques that may be implementedutilizing the previously described systems and devices. Aspects of eachof the procedures may be implemented in hardware, firmware, or software,or a combination thereof. The procedures are shown as a set of blocksthat specify operations performed by one or more devices and are notnecessarily limited to the orders shown for performing the operations bythe respective blocks. In portions of the following discussion,reference will be made to FIGS. 1-18 .

FIG. 19 depicts a procedure 1900 in an example implementation in which arelative time is specified in relation to an event to cause output ofevents that correspond to the relative time. One or more inputs arereceived, at one or more computing devices, that involve interactionassociated with a particular one of a plurality of events via a userinterface, in which the plurality of events result from a search ofdata, each of the plurality of events include the data that isassociated with a respective point in time, and the one or more inputsspecify a relative time in relation to the respective point in time ofthe particular event (block 1902). For example, a user may interact witha search UI 108 of a client application module 106. The search UI 108may be configured to include options to specify the relative point intime, such as option 1102, option 1202, option 1502, option 1602, and soon. Inputs resulting from this interaction may then be communicated viaa network to system 100 for processing, may be performed locally, and soon.

A determination is made as to which of the plurality of eventscorrespond to the specified relative time by the one or more computingdevices (block 1904). This may be performed as a new search in which therelative time is a sole query for the search, automatically added aspart of a previous search, and so on. Further, this search may beperformed by the system 100, locally at the client application module106, and so forth.

A result of the determination is then output by the one or morecomputing devices for display in the user interface (block 1906). Thesystem 100, for instance, may generate the output for communication viaa network to the client application module 106 for output in the searchUI 108, such as to display the events and corresponding data thatcorresponds to the relative time, e.g., falls with a time range, is “at”the point in time, and so forth. A variety of other examples are alsocontemplated without departing from the spirit and scope of thetechniques described herein.

Example System and Device

FIG. 20 illustrates an example system generally at 2000 that includes anexample computing device 2002 that is representative of one or morecomputing systems and/or devices that may implement the varioustechniques described herein. This is illustrated through inclusion ofthe search interface module 2004 that is representative of functionalityto interact with a search service 2006, e.g., to specify and managesearches using a late-binding schema and events as described above andthus may correspond to the client application module 106 and system 100of FIG. 1 . The computing device 2002 may be, for example, a server of aservice provider, a device associated with a client (e.g., a clientdevice), an on-chip system, and/or any other suitable computing deviceor computing system.

The example computing device 2002 as illustrated includes a processingsystem 2008, one or more computer-readable media 2010, and one or moreI/O interface 2012 that are communicatively coupled, one to another.Although not shown, the computing device 2002 may further include asystem bus or other data and command transfer system that couples thevarious components, one to another. A system bus can include any one orcombination of different bus structures, such as a memory bus or memorycontroller, a peripheral bus, a universal serial bus, and/or a processoror local bus that utilizes any of a variety of bus architectures. Avariety of other examples are also contemplated, such as control anddata lines.

The processing system 2008 is representative of functionality to performone or more operations using hardware. Accordingly, the processingsystem 2008 is illustrated as including hardware element 2014 that maybe configured as processors, functional blocks, and so forth. This mayinclude implementation in hardware as an application specific integratedcircuit or other logic device formed using one or more semiconductors.The hardware elements 2014 are not limited by the materials from whichthey are formed or the processing mechanisms employed therein. Forexample, processors may be comprised of semiconductor(s) and/ortransistors (e.g., electronic integrated circuits (ICs)). In such acontext, processor-executable instructions may beelectronically-executable instructions.

The computer-readable storage media 2010 is illustrated as includingmemory/storage 2016. The memory/storage 2016 represents memory/storagecapacity associated with one or more computer-readable media. Thememory/storage component 2016 may include volatile media (such as randomaccess memory (RAM)) and/or nonvolatile media (such as read only memory(ROM), Flash memory, optical disks, magnetic disks, and so forth). Thememory/storage component 2016 may include fixed media (e.g., RAM, ROM, afixed hard drive, and so on) as well as removable media (e.g., Flashmemory, a removable hard drive, an optical disc, and so forth). Thecomputer-readable media 2010 may be configured in a variety of otherways as further described below.

Input/output interface(s) 2012 are representative of functionality toallow a user to enter commands and information to computing device 2002,and also allow information to be presented to the user and/or othercomponents or devices using various input/output devices. Examples ofinput devices include a keyboard, a cursor control device (e.g., amouse), a microphone, a scanner, touch functionality (e.g., capacitiveor other sensors that are configured to detect physical touch), a camera(e.g., which may employ visible or non-visible wavelengths such asinfrared frequencies to recognize movement as gestures that do notinvolve touch), and so forth. Examples of output devices include adisplay device (e.g., a monitor or projector), speakers, a printer, anetwork card, tactile-response device, and so forth. Thus, the computingdevice 2002 may be configured in a variety of ways as further describedbelow to support user interaction.

Various techniques may be described herein in the general context ofsoftware, hardware elements, or program modules. Generally, such modulesinclude routines, programs, objects, elements, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. The terms “module,” “functionality,” and“component” as used herein generally represent software, firmware,hardware, or a combination thereof. The features of the techniquesdescribed herein are platform-independent, meaning that the techniquesmay be implemented on a variety of commercial computing platforms havinga variety of processors.

An implementation of the described modules and techniques may be storedon or transmitted across some form of computer-readable media. Thecomputer-readable media may include a variety of media that may beaccessed by the computing device 2002. By way of example, and notlimitation, computer-readable media may include “computer-readablestorage media” and “computer-readable signal media.”

“Computer-readable storage media” may refer to media and/or devices thatenable persistent and/or non-transitory storage of information incontrast to mere signal transmission, carrier waves, or signals per se.Thus, computer-readable storage media refers to non-signal bearingmedia. The computer-readable storage media includes hardware such asvolatile and non-volatile, removable and non-removable media and/orstorage devices implemented in a method or technology suitable forstorage of information such as computer readable instructions, datastructures, program modules, logic elements/circuits, or other data.Examples of computer-readable storage media may include, but are notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, harddisks, magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or other storage device, tangible media, orarticle of manufacture suitable to store the desired information andwhich may be accessed by a computer.

“Computer-readable signal media” may refer to a signal-bearing mediumthat is configured to transmit instructions to the hardware of thecomputing device 2002, such as via a network. Signal media typically mayembody computer readable instructions, data structures, program modules,or other data in a modulated data signal, such as carrier waves, datasignals, or other transport mechanism. Signal media also include anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media include wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, and other wireless media.

As previously described, hardware elements 2014 and computer-readablemedia 2010 are representative of modules, programmable device logicand/or fixed device logic implemented in a hardware form that may beemployed in some embodiments to implement at least some aspects of thetechniques described herein, such as to perform one or moreinstructions. Hardware may include components of an integrated circuitor on-chip system, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), and other implementations in silicon or other hardware.In this context, hardware may operate as a processing device thatperforms program tasks defined by instructions and/or logic embodied bythe hardware as well as a hardware utilized to store instructions forexecution, e.g., the computer-readable storage media describedpreviously.

Combinations of the foregoing may also be employed to implement varioustechniques described herein. Accordingly, software, hardware, orexecutable modules may be implemented as one or more instructions and/orlogic embodied on some form of computer-readable storage media and/or byone or more hardware elements 2014. The computing device 2002 may beconfigured to implement particular instructions and/or functionscorresponding to the software and/or hardware modules. Accordingly,implementation of a module that is executable by the computing device2002 as software may be achieved at least partially in hardware, e.g.,through use of computer-readable storage media and/or hardware elements2014 of the processing system 2008. The instructions and/or functionsmay be executable/operable by one or more articles of manufacture (forexample, one or more computing devices 2002 and/or processing systems2008) to implement techniques, modules, and examples described herein.

The techniques described herein may be supported by variousconfigurations of the computing device 2002 and are not limited to thespecific examples of the techniques described herein. This functionalitymay also be implemented all or in part through use of a distributedsystem, such as over a “cloud” 2018 via a platform 2020 as describedbelow.

The cloud 2018 includes and/or is representative of a platform 2020 forresources 2022. The platform 2020 abstracts underlying functionality ofhardware (e.g., servers) and software resources of the cloud 2018. Theresources 2022 may include applications and/or data that can be utilizedwhile computer processing is executed on servers that are remote fromthe computing device 2002. Resources 2022 can also include servicesprovided over the Internet and/or through a subscriber network, such asa cellular or Wi-Fi network.

The platform 2020 may abstract resources and functions to connect thecomputing device 2002 with other computing devices. The platform 2020may also serve to abstract scaling of resources to provide acorresponding level of scale to encountered demand for the resources2022 that are implemented via the platform 2020. Accordingly, in aninterconnected device embodiment, implementation of functionalitydescribed herein may be distributed throughout the system 2000. Forexample, the functionality may be implemented in part on the computingdevice 2002 as well as via the platform 2020 that abstracts thefunctionality of the cloud 2018.

CONCLUSION

Although the invention has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the invention defined in the appended claims is not necessarilylimited to the specific features or acts described. Rather, the specificfeatures and acts are disclosed as example forms of implementing theclaimed invention.

What is claimed is:
 1. A computer-implemented method comprising: causingdisplay of a first set of events in a graphical user interface (GUI),wherein an event of the first set of events is associated with atimestamp; in response to receiving a first selection of arepresentation of a particular timestamp associated with a particularevent, causing display of an option that is selectable to specify arelative time in relation to the particular timestamp, wherein therepresentation of the particular timestamp is displayed in associationwith the display of the first set of events; and in response toreceiving a second selection that corresponds to the option, causingdisplay of a second set of events based on a determination that thesecond set of events corresponds to the relative time.
 2. The method ofclaim 1, wherein the representation of the particular timestamp isdisplayed in association with the particular event.
 3. The method ofclaim 1, further comprising: receiving, in the GUI, an interaction withthe particular event in the display of the first set of events; based onthe interaction, displaying the representation of the particulartimestamp.
 4. The method of claim 1, wherein the causing of the displayof the first set of events in the GUI includes displaying therepresentation of the particular timestamp in the GUI.
 5. The method ofclaim 1, wherein the particular timestamp is extracted from a portion ofraw data of the particular event.
 6. The method of claim 1, wherein therepresentation of the particular timestamp is included in a presentationof a plurality of fields corresponding to a portion of raw machine dataof the particular event.
 7. The method of claim 1, wherein the optionincludes a representation of a range of time that is selectable tospecify the relative time in relation to the particular timestamp. 8.The method of claim 1, wherein the option is selectable to specify thatthe relative time is in relation to both before and after the particulartimestamp.
 9. The method of claim 1, wherein the second selectionassociated with the option causes an automatic determination, via one ormore computing devices, of the relative time as an amount of time inrelation to the particular timestamp without manual entry of therelative time.
 10. The method of claim 1, wherein the option isdisplayed proximate to the representation of the particular timestamp inthe GUI.
 11. A computer-implemented system comprising: a processordevice; and a computer-readable storage medium having instructionsstored thereon which, when executed by the processor device, causeperformance of operations comprising: causing display of a first set ofevents in a graphical user interface (GUI), wherein an event of thefirst set of events is associated with a timestamp; in response toreceiving a first selection of a representation of a particulartimestamp associated with a particular event, causing display of anoption that is selectable to specify a relative time in relation to theparticular timestamp, wherein the representation of the particulartimestamp is displayed in association with the display of the first setof events; and in response to receiving a second selection thatcorresponds to the option, causing display of a second set of eventsbased on a determination that the second set of events corresponds tothe relative time.
 12. The system of claim 11, wherein therepresentation of the particular timestamp is displayed in associationwith the particular event.
 13. The system of claim 11, where theoperations further comprise: receiving, in the GUI, an interaction withthe particular event in the display of the first set of events; based onthe interaction, displaying the representation of the particulartimestamp.
 14. The system of claim 11, wherein the causing of thedisplay of the first set of events in the GUI includes displaying therepresentation of the particular timestamp in the GUI.
 15. The system ofclaim 11, wherein the representation of the particular timestamp isincluded in a presentation of a plurality of fields corresponding to aportion of raw machine data of the particular event.
 16. One or morenon-transitory computer-readable storage media comprising instructionsthat are stored thereon that, responsive to execution by a computingdevice, causes the computing device to perform operations comprising:causing display of a first set of events in a graphical user interface(GUI), wherein an event of the first set of events is associated with atimestamp; in response to receiving a first selection of arepresentation of a particular timestamp associated with a particularevent, causing display of an option that is selectable to specify arelative time in relation to the particular timestamp, wherein therepresentation of the particular timestamp is displayed in associationwith the display of the first set of events; and in response toreceiving a second selection that corresponds to the option, causingdisplay of a second set of events based on a determination that thesecond set of events corresponds to the relative time.
 17. The one ormore computer-readable storage media of claim 16, wherein therepresentation of the particular timestamp is displayed in associationwith the particular event.
 18. The one or more computer-readable storagemedia of claim 16, where the operations further comprise: receiving, inthe GUI, an interaction with the particular event in the display of thefirst set of events; based on the interaction, displaying therepresentation of the particular timestamp.
 19. The one or morecomputer-readable storage media of claim 16, wherein the causing of thedisplay of the first set of events in the GUI includes displaying therepresentation of the particular timestamp in the GUI.
 20. The one ormore computer-readable storage media of claim 16, wherein therepresentation of the particular timestamp is included in a presentationof a plurality of fields corresponding to a portion of raw machine dataof the particular event.